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ESMValTool

  • Introduction
  • ESMValTool Functionalities
  • Getting started
  • Gallery
  • Available recipes
  • Obtaining input data
  • Making a recipe or diagnostic
  • Contributing to the community
  • Utilities
  • Diagnostics API Reference
    • Shared Diagnostic Code
    • Diagnostic Scripts
      • Emergent constraints diagnostics
      • Machine Learning Regression (MLR) diagnostics
        • Examples
        • Diagnostic scripts
        • Auxiliary scripts
        • Available MLR models
      • Monitor Diagnostic
      • Ocean diagnostics toolkit
      • Psyplot Diagnostic
  • Frequently Asked Questions
  • Changelog

ESMValCore

  • Getting started
  • The recipe format
  • Diagnostic script interfaces
  • Development
  • Contributing
  • ESMValCore API Reference
  • Changelog
ESMValTool
  • ESMValTool Code API Documentation
  • Machine Learning Regression (MLR) diagnostics
  • Edit on GitHub

Machine Learning Regression (MLR) diagnostics

This module provides various tools to create and evaluate MLR models for arbitrary input variables.

Examples

  • Constraining uncertainty in projected gross primary production (GPP) with machine learning: Use Gradient Boosted Regression Tree (GBRT) algorithm to constrain projected Gross Primary Production (GPP) in RCP 8.5 scenario using observations of process-based predictors.

Diagnostic scripts

  • Evaluate residuals
  • MLR main diagnostic
  • Multi-model means (MMM)
  • Plotting functionalities
  • Postprocessing functionalities
  • Preprocessing functionalities
  • Rescale data with emergent constraints

Auxiliary scripts

  • Auxiliary functions for MLR scripts
  • Custom extensions of sklearn functionalities
  • MLRModel base class
  • Base class for Gradient Boosted Regression models
  • Base class for Linear models

Available MLR models

  • Gradient Boosted Regression Trees (sklearn implementation)
  • Gradient Boosted Regression Trees (xgboost implementation)
  • Gaussian Process Regression (sklearn implementation)
  • Huber Regression
  • Kernel Ridge Regression
  • LASSO Regression
  • LASSO Regression with built-in CV
  • LASSO Regression (using Least-angle Regression algorithm) with built-in CV
  • Linear Regression
  • Random Forest Regression
  • Ridge Regression
  • Ridge Regression with built-in CV
  • Support Vector Regression
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